Capital structure construct: a new approach to behavioral finance

Reyes, José Anselmo Pérez; Miranda, Montserrat Reyna; Martínez, Jorge Vera

doi:10.21511/imfi.16(4).2019.08

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…Behavioral finance is still among the interesting areas to explore (Xing, 2020). Reyes et al (2019) found that financial behavior can be studied as a cognitive structure. Thus, a multidimensional "financial-cognitive" structure can be created using specific variables.…”

Section: Capital Structure Legal Environment Theorymentioning

confidence: 99%

“…Thus, a multidimensional "financial-cognitive" structure can be created using specific variables. Their model explained how decision-makers reacted in the face of environmental conditions and uncertainty and found that they directly impacted the loss of revenue assessment of the firm (Reyes et al, 2019). Jurevičienė et al (2014) found that behavioral financial affairs are significant at both corporate and individual levels.…”

Section: Capital Structure Legal Environment Theorymentioning

confidence: 99%

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Identification of the Factors Affecting Capital Structure in Firms with Emphasis on the Role of Behavioral Factors

Ahmadi,

Mohammadi,

Mokhatab Rafei

2023

IJFIFSA

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Making decisions regarding capital structure is among the most challenging issues ahead for firms and the most critical decisions for their survival. On the other hand, several significant aspects, such as behavioral factors, have been overlooked in this field. Thus, the present study mainly seeks to identify the factors affecting capital structure in Iranian firms, emphasizing the role of behavioral factors. The present study employs mixed qualitative and quantitative research methods. From the qualitative point of view, capital market experts were inquired, and theoretical saturation was achieved using the snowball method. After the interviews, research components were extracted through coding. The opinions of a group of experts and managers of firms listed on the Tehran Stock Exchange were used in the quantitative section, and a structural equation form was used to perform confirmatory factor analysis on the research model. A total of 63 concepts in the form of six categories were identified at the first stage, which was reduced to 58 in the form of six categories and was confirmed after the concepts were sent back to the experts. The principal components included behavioral factors, macroeconomic factors, political factors, socio-cultural factors, firm features, and corporate governance. Results were validated through factor analysis in the quantitative portion of the study. The present study can be considered among the comprehensive studies at the construct level with an integrated approach to firms' capital structure. The emergence of behavioral finance resulted from understanding the importance of measuring human behavior as a factor with transcendent consequences for financial decisions. Hence, most behavioral finance studies are focused on observable behaviors. However, the item response theory presents an integrated method for disciplines that work with cognitive variables. Accepting opportunities for new knowledge is essential for firm decisions to respond to the mental views of financial managers.The present study sought to identify the factors influencing firms' capital structure in Iran. The tool used in the present study reflected the elements making up the capital structure. In this regard, the notable point is how the classic criterion of structural capital components can explain financial managers' perception of decision-making. The research results in this area are interesting since we have confirmed a capital structure theory at the construct level. The conformity of the results and the obtained reliability levels indicate that this theory fits the given dimensions well. Moreover, relevant evidence indicates that senior financial managers adopt various states considering internal and external factors at the structural level, which can cause cognitive bias in decision-making.

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Section: Capital Structure Legal Environment Theorymentioning

confidence: 99%

Section: Capital Structure Legal Environment Theorymentioning

confidence: 99%

Identification of the Factors Affecting Capital Structure in Firms with Emphasis on the Role of Behavioral Factors

Ahmadi,

Mohammadi,

Mokhatab Rafei

2023

IJFIFSA

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Analysis of Structural Shifts in Students’ Behavioral Patterns During COVID-19

Liashenko

Kravets

Prokopenko

2021

2021 11th International Conference on Advanced Computer Information Technologies (ACIT)

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Consumer behavior clustering of food retail chains by machine learning algorithms

Liashenko¹,

Kravets²,

Prokopenko³

2021

ACCESS

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Analysis of the behavior of an economic agent is one of the central themes of microeconomics. Right now, with the comprehensive increase in the amount of data and the expansion of the computing capabilities of personal computers, there is a need to implement methods of behavioral economics in the study of human behavior. In the course of this study, a survey was created aimed at identification of patterns of behavior of the modern consumer according to his selection criteria stores and reactions to questions based on Behavioral Economics theorems. Clustering the obtained results were performed using machine learning algorithms, after which the Random Forest classification algorithm was trained. According to the results of Silhouette analysis, K-means clusters were selected as the main ones for further modeling. T-SNE algorithms, hierarchical and spectral analysis were used for additional visual representation. This study offers a tool for classifying customer preferences and analyzing current industry trends. A tool has been created to classify consumers of food retail chains in order to improve their "buyer's journey" and better understand their needs. The created tool for clustering and classification by machine learning methods can be used in business processes. To improve the result, it is necessary to choose a more representative sample, because used in this study consists of an average rationally thinking and knowledgeable individuals, which cannot be said of the average consumer not only among the older generation but also among the younger. Therefore, the next directions in the study may be to identify new ones behavioral trends in other industries; deepening understanding of food retail; use of geodata to improve analysis, etc. Potentially attractive the direction may be to add an assessment of the impact of network advertising on behavior consumers through semantics analysis and image recognition.

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Capital structure construct: a new approach to behavioral finance

Cited by 3 publications

References 19 publications

Identification of the Factors Affecting Capital Structure in Firms with Emphasis on the Role of Behavioral Factors

Identification of the Factors Affecting Capital Structure in Firms with Emphasis on the Role of Behavioral Factors

Analysis of Structural Shifts in Students’ Behavioral Patterns During COVID-19

Consumer behavior clustering of food retail chains by machine learning algorithms

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